A Bilevel Programming Framework for Determining the Optimal Incentive-Based Traffic Demand Management Strategy
Bibliographic record
Abstract
Incentive-based traffic demand management (IBTDM) is a cost-effective alternative to increasing capacities and conventional traffic demand management strategies. This paper focuses on IBTDM strategy to provide incentives for commuting drivers’ departure time shifts to balance temporal distribution of demand by proposing a bilevel programming framework to obtain optimal IBTDM strategy and to evaluate IBTDM strategy’s impact on commuters’ departure time choice behavior. In the upper-level, the objective function is to minimize total travel time with the total monetary compensation constraint by a pre-set budget, while decision variables are time-varying incentives for commuters according to their departure times. The optimal time-varying incentive profile is then passed on to the lower-level, within which the decision variable is personal departure time choice. The result indicates that such a time-varying linear incentive profile that reaches the highest at the “shoulders” of peak period while remains lowest during the most peak period achieves superior performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".